Data
analysis is the second major step in assessing goose damage on crop yields (Figure IIA-25). The goal of data analysis is to derive as much information from the given data as possible to assist in separating goose damage from other factors. GIS provides the tools for data analysis. Establishing relationships between the data and the yield, comparing data, or calculating a statistical summary are examples of GIS data analysis techniques that can be applied.

What is GIS?Geographic information systems (GIS) provide a systematic approach to managing large amounts of data, along with the tools necessary for analysis and interpretation. This guideline reviews some example data sets used to characterize a field, showing how GIS can help organize and manage the data so as to be used more effectively in various management decisions .

What is a query?
The
query function is an integral part of any database. It is used to select a group of records from the tables within a database. Several applications use queries to select yield features by their value or location. Queries can be performed on the yield data to determine whether a relationship exists between certain soil types and crop yield.

Importance of a query
Selecting records from a database or map allows further analysis of the retrieved items. A small area of exceptional yield might be explained by soil conditions found in the same location. Within the GIS environment, locating areas of high or low yield can be done by running a query. Following the query on yield levels, one can define a query that identifies specific soil conditions. Running these two queries on the yield data is the first step to seeking a relationship between yield amounts and soil types.

There are several other reasons to query your GIS data.
Among these reasons are:

-

To use the results for further analysis

-

To use the results to select other
features

-

To make edits to the selected data

-

To calculate statistics on the selected
data

-

To create a graph or report that describes
your selection

-

To export your selection to a new
file

For example, it is possible to generate summary statistics on the yield points that were selected previously through a query. After the statistics are compiled, it is possible to select the soils that intersect the selected yield points. No matter which results are desired, most spatial analysis starts with some form of query.

Performing a Query

There are two ways to perform
a query:

1.

Query using the Select by Theme

2.

Query using the Editing mode

1. Query using the Select by
Theme:

Step 1 : Select by Theme

Select the yield points that intersect the
selected soils or the time of grazing

Drag the wheat yield (point) theme to the
top of the table of contents and make it the
active theme

Choose Select by Theme from
the Theme menu

Step 2 : Enter Input Data

From the first drop-down list (Select features
of active themes that), choose Intersect

Click New Set

Based on the choices, ArcView selects the yield points that touch or are within the boundary of the selected polygons in the Soil Map theme.

Step 3 : Open Attribute Table

View the selection in the attribute table

It is also possible to view the selection
in the theme's attribute table

Make the Yield data (point) active

Click the Open Theme Table button

To make it easier to view the selection, promote
the selection to the top of the table by clicking
the Promote button

The selected records are all highlighted and
grouped together at the top of the table

2. Query using the editing
mode

Step 1 : Start an Editing Cession

Highlight the yield data theme

From the menu bar, select
Theme and click on Start Editing

Step 2 : Open Attribute Table

Open the table by clicking on this button

It is possible to make a new theme containing only the yield inside exclosures

Step 3 : Start a Query

Start a query by clicking on the button pictured

For instance, yield inside the exclosures was flagged with number 2

Click on New Set

Change selection by clicking on

Step 4 : Delete Record

From the editing tool bar select Delete Records

Step 5 : Save Edits and Display the New Shapefile

From the table menu choose Save Edits As (give it a new name).

Display the new shapefile.

Sorting Yield Data

Whether using method 1,Query by theme, or Method 2,
Query using edit mode, it is possible to perform the
following:

1.

Remove data impacted by confounding factors

2.

Sort data by soil type

3.

Sort data by intensity of grazing

4.

Get estimate of yield from each exclosure
and its pair

1. Remove confounding factors

Several factors may affect yield other than goose grazing. The extent of impact from these factors should be carefully delineated and their yield separated before any further analysis.

For instance, water inundation (ponded area) has a negative impact on yield. Areas impacted by water should be clipped out and not considered in the evaluation.

Step 1 :Display Areas Impacted by Water

Step 2 : Add the Yield Data Layer

Step 3 : Clip (delete) Yield in Water Impacted Areas

Need ArcView extensions XTools (clip with polygons, erase…)

From the XTools menu, click on Erase Features

Step 4 : Display Yield Data

After clipping out the areas impacted by water, the yield data map should look like this:

2. Test for soil differences

One of the most basic sources of potential yield variability is the difference in soil types within the field. The data files associated with the soil types provide a detailed database of soil characteristics that can be related to other observations and measurements.

Depending on field characteristics for each exclosure location per soil type, there are two ways to test for soil differences:

Perform a t-test

For field having only two major soil types, perform a t-test compare yield inside exclosure for each soil type (Figure IIA-26).

For fields having more than two soil types, perform an ANOVA using the yield from each exclosure. In many cases, it may not be possible to perform an ANOVA based on the exclosure yield. However, it is still possible to obtain reliable information about the differences in yield based on soil types by collecting data from a representative subset or sample. Sampling is simply the technique used to choose representative yield data points for analysis from a larger population. In a GIS environment, it is possible to randomly select a sub-sample from the population using bootstrapping.

What is Bootstrapping?

Bootstrapping is a resampling procedure that pulls random observations from an entire data set. This procedure generally is applied to large datasets with many observations. Electronic, automatic sampling such as yield mapping often generates this type of data. Bootstrap methods are computer-intensive processes of obtaining a subset of the data that meets statistical assumptions and can be used to calculate standard errors, confidence intervals, and significance tests. Each subsample is a random sample with replacement from the full sample.

How it is done in Arc/View?

Step 1: From the movement menu select create
bootstrap file

Step 2: Insert input data

A bootstrap file creator window will open.

Specify the theme name, the number of iteration to
be performed, and the sample size.

Step 3: Name and save the output
bootstrap feature.

No soil Difference

In case there is no significant yield difference due to soil type, we can combine the yield data from all exclosures for further analysis.

3. Sort data by intensity of grazing

Since most of the necessary data has been collected in the field or subsequently generated, it is possible to use the data to determine whether relationships exist between goose grazing and yields.

This step can be performed using a query or a clip command. Figure IIA-27 illustrates the process of using the clip command. (a) Polygon theme (areas grazed in March) used to clip yield data point. (b) Output map representing yield data within a particular class of interest (in this case, areas grazed in March).

Figure IIA-27: Map showing March grazing (left) and yield data clipped for the area grazed in March (right).

Using GIS to extract yield data in a series of steps from within zones of impact, we can determine yields from areas grazed in April, areas grazed in March but not April, areas grazed in January but not later, areas in exclosures, and other nongrazed areas (Figure IIA-28). This procedure allows us to develop a complete picture of goose grazing impact on yields and to evaluate the effects of seasonal grazing.

Figure IIA-28: Summary table comparing the impact of timing of grazing on yield.

0

Exclosure

No
to Light Grazing

Moderate
Grazing

Heavy
Grazing

Water
Impact

#
of Observations

85

2454

6831

2435

464

Yield
(g/m2)

513

483

452

374

303

STDV

82

63

54

86

83

4. Obtain estimate of yield using paired
plot comparisons

The same exclosure (control) can be used for more than just one-pair comparisons.